SoroushVahidi/scidocs huggingface dataset:
A Secure Color Image Steganography In Transform Domain
(2013)

Steganography is the art and science of covert communication. The secret information can be concealed in content such as image, audio, or video. This paper provides a novel image steganography technique to hide both image and key in color cover image using Discrete Wavelet Transform (DWT) and Integer Wavelet Transform (IWT). There is no visual difference between the stego image and the cover image. The extracted image is also similar to the secret image. This is proved by the high PSNR (Peak Signal to Noise Ratio), value for both stego and extracted secret image. The results are compared with the results of similar techniques and it is found that the proposed technique is simple and gives better PSNR values than others.
Multi-document English Text Summarization using Latent Semantic Analysis
(2015)

In today's busy schedule, everybody expects to get the information in short but meaningful manner. Huge long documents consume more time to read. For this, we need summarization of document. Work has been done on Single-document but need of multiple document summarization is encouraging. Existing methods such as cluster approach, graph-based approach and fuzzy-based approach for multiple document summaries are improving. The statistical approach based on algebraic method is still topic of research. It demands for improvement in the approach by considering the limitations of Latent Semantic Analysis (LSA). Firstly, it reads only input text and does not consider world knowledge, for example women and lady it does not consider synonyms. Secondly, it does not consider word order, for example I will deliver to you tomorrow, deliver I will to you or tomorrow I will deliver to you. These different clauses may wrongly convey same meaning in different parts of document. Experimental results have overcomed the limitation and prove LSA with tf-idf method better in performance than KNN with tf-idf.
JSForce: A Forced Execution Engine for Malicious JavaScript Detection
(2017)

The drastic increase of JavaScript exploitation attacks has led to a strong interest in developing techniques to enable malicious JavaScript analysis. Existing analysis techniques fall into two general categories: static analysis and dynamic analysis. Static analysis tends to produce inaccurate results (both false positive and false negative) and is vulnerable to a wide series of obfuscation techniques. Thus, dynamic analysis is constantly gaining popularity for exposing the typical features of malicious JavaScript. However, existing dynamic analysis techniques possess limitations such as limited code coverage and incomplete environment setup, leaving a broad attack surface for evading the detection. To overcome these limitations, we present the design and implementation of a novel JavaScript forced execution engine named JSForce which drives an arbitrary JavaScript snippet to execute along different paths without any input or environment setup. We evaluate JSForce using 220,587 HTML and 23,509 PDF realworld samples. Experimental results show that by adopting our forced execution engine, the malicious JavaScript detection rate can be substantially boosted by 206.29% using same detection policy without any noticeable false positive increase. We also make JSForce publicly available as an online service and will release the source code to the security community upon the acceptance for publication.
Evaluation of Cluster Identification Performance for Different PCP Variants
(2010)

Parallel coordinate plots (PCPs) are a well-known visualization technique for viewing multivariate data. In the past, various visual modifications to PCPs have been proposed to facilitate tasks such as correlation and cluster identification, to reduce visual clutter, and to increase their information throughput. Most modifications pertain to the use of color and opacity, smooth curves, or the use of animation. Although many of these seem valid improvements, only few user studies have been performed to investigate this, especially with respect to cluster identification. We performed a user study to evaluate cluster identification performance – with respect to response time and correctness – of nine PCP variations, including standard PCPs. To generate the variations, we focused on covering existing techniques as well as possible while keeping testing feasible. This was done by adapting and merging techniques, which led to the following novel variations. The first is an effective way of embedding scatter plots into PCPs. The second is a technique for highlighting fuzzy clusters based on neighborhood density. The third is a spline-based drawing technique to reduce ambiguity. The last is a pair of animation schemes for PCP rotation. We present an overview of the tested PCP variations and the results of our study. The most important result is that a fair number of the seemingly valid improvements, with the exception of scatter plots embedded into PCPs, do not result in significant performance gains.
Study of bi-directional buck-boost converter with different control methods
(2008)

In this paper, four types control methods have been proposed for buck-boost cascade topology. This converter used in hybrid electric vehicle can step the battery voltage level either up or down. The target is to find the most appropriate control method for the converter with certain specifications. Critical conduction mode control, fixed frequency control, the buck/boost control and the buck + boost control have been mentioned. The inductor design is presented according to the given parameters. The relationship among the parameters for buck/boost mode, buck mode and boost mode in critical conduction mode are proposed. The efficiency comparison for four control methods is given. The loss breakdown of different control methods is also presented by mathematical calculation.
A user-guided approach to program analysis
(2015)

Program analysis tools often produce undesirable output due to various approximations. We present an approach and a system EUGENE that allows user feedback to guide such approximations towards producing the desired output. We formulate the problem of user-guided program analysis in terms of solving a combination of hard rules and soft rules: hard rules capture soundness while soft rules capture degrees of approximations and preferences of users. Our technique solves the rules using an off-the-shelf solver in a manner that is sound (satisfies all hard rules), optimal (maximally satisfies soft rules), and scales to real-world analyses and programs. We evaluate EUGENE on two different analyses with labeled output on a suite of seven Java programs of size 131–198 KLOC. We also report upon a user study involving nine users who employ EUGENE to guide an information-flow analysis on three Java micro-benchmarks. In our experiments, EUGENE significantly reduces misclassified reports upon providing limited amounts of feedback.
Fast efficient algorithm for enhancement of low lighting video
(2010)

We describe a novel and effective video enhancement algorithm for low lighting video. The algorithm works by first inverting the input low-lighting video and then applying an image de-haze algorithm on the inverted input. To facilitate faster computation and improve temporal consistency, correlations between temporally neighboring frames are utilized. Simulations using naive implementations of the algorithm show good enhancement results and 2x speed-up as compared with frame-wise enhancement algorithms, with further improvements in both quality and speed possible.
Prediction of heart disease using data mining techniques
(2016)

The healthcare industry is a vast field with a plethora of data about patients,added to the huge medical records every passing day. In terms of science, this industry is ’information rich’ yet ’knowledge poor’. However, data mining with its various analytical tools and techniques plays a major role in reducing the use of cumbersome tests used on patients to detect a disease. The aim of this paper is to employ and analyze different data mining techniques for the prediction of heart disease in a patient through extraction of interesting patterns from the dataset using vital parameters. This paper strives to bring out the methodology and implementation of these techniques-Artificial Neural Networks, Decision Tree and Naive Bayes and stress upon the results and conclusion induced on the basis of accuracy and time complexity. By far, the observations reveal that Artificial Neural Networks outperformed Naive Bayes and Decision Tree.
Design of a novel dual band patch antenna loaded with CELC resonator for WLAN/WiMAX applications
(2014)

A Complementary Electric Field Coupled (CELC) resonator, loaded in the ground plane of a conventional patch antenna to achieve dual band operation is presented here. Two antenna designs have been presented. In the first design, one CELC is etched out from the ground plane of patch antenna which leads to generation of a lower order mode in addition to the conventional patch mode. In order to achieve better impedance bandwidth for the lower order resonant mode two CELC resonators with closely spaced resonant frequencies are etched out from the ground plane of patch antenna in the second design. The proposed antennas can be used in WLAN (Wireless Local Area Network) and WiMAX (Worldwide Interoperability for Microwave Access) applications.
Quantitative and qualitative research: beyond the debate.
(2008)

Psychology has been a highly quantitative field since its conception as a science. However, a qualitative approach to psychological research has gained increasing importance in the last decades, and an enduring debate between quantitative and qualitative approaches has arisen. The recently developed Mixed Methods Research (MMR) addresses this debate by aiming to integrate quantitative and qualitative approaches. This article outlines and discusses quantitative, qualitative and mixed methods research approaches with specific reference to their (1) philosophical foundations (i.e. basic sets of beliefs that ground inquiry), (2) methodological assumptions (i.e. principles and formal conditions which guide scientific investigation), and (3) research methods (i.e. concrete procedures for data collection, analysis and interpretation). We conclude that MMR may reasonably overcome the limitation of purely quantitative and purely qualitative approaches at each of these levels, providing a fruitful context for a more comprehensive psychological research.
A generic abstract syntax model for embedded languages
(2012)

Representing a syntax tree using a data type often involves having many similar-looking constructors. Functions operating on such types often end up having many similar-looking cases. Different languages often make use of similar-looking constructions. We propose a generic model of abstract syntax trees capable of representing a wide range of typed languages. Syntactic constructs can be composed in a modular fashion enabling reuse of abstract syntax and syntactic processing within and across languages. Building on previous methods of encoding extensible data types in Haskell, our model is a pragmatic solution to Wadler's "expression problem". Its practicality has been confirmed by its use in the implementation of the embedded language Feldspar.
Semantic File Systems
(1991)

A semantic file system is an information storage system that provides flexible associative access to the system's contents by automatically extracting attributes from files with file type specific transducers. Associative access is provided by a conservative extension to existing tree-structured file system protocols, and by protocols that are designed specifically for content based access. Compatiblity with existing file system protocols is provided by introducing the concept of a virtual directory. Virtual directory names are interpreted as queries, and thus provide flexible associative access to files and directories in a manner compatible with existing software. Rapid attribute-based access to file system contents is implemented by automatic extraction and indexing of key properties of file system objects. The automatic indexing of files and directories is called "semantic" because user programmable transducers use information about the semantics of updated file system objects to extract the properties for indexing. Experimental results from a semantic file system implementation support the thesis that semantic file systems present a more effective storage abstraction than do traditional tree structured file systems for information sharing and command level programming.
Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality
(1998)

The nearest neighbor problem is the follolving: Given a set of n points P = (PI, . . . ,p,} in some metric space X, preprocess P so as to efficiently answer queries which require finding bhe point in P closest to a query point q E X. We focus on the particularly interesting case of the d-dimensional Euclidean space where X = Wd under some Zp norm. Despite decades of effort, t,he current solutions are far from saabisfactory; in fact, for large d, in theory or in practice, they provide litt,le improvement over the brute-force algorithm which compares the query point to each data point. Of late, t,here has been some interest in the approximate newest neighbors problem, which is: Find a point p E P that is an c-approximate nearest neighbor of the query q in t,hat for all p’ E P, d(p, q) < (1 + e)d(p’, q). We present two algorithmic results for the approximate version t,hat significantly improve the known bounds: (a) preprocessing cost polynomial in n and d, and a truly sublinear query t.ime (for 6 > 1); and, (b) query time polynomial in log-n and d, and only a mildly exponential preprocessing cost* O(n) x 0(1/~)~. Furt.her, applying a classical geometric lemma on random projections (for which we give a simpler proof), we obtain t.he first known algorithm with polynomial preprocessing and query t.ime polynomial in d and log n. Unfortunately, for small E, the latter is a purely theoretical result since bhe e?rponent depends on l/e. Experimental resuits indicate that our tit algori&m offers orders of magnitude improvement on running times over real data sets. Its key ingredient is the notion of locality-sensitive hashing which may be of independent interest; here, we give applications to information ret,rieval, pattern recognition, dynamic closest-pairs, and fast clustering algorithms. *Supported by a Stanford Graduate Fellowship and NSF
Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search
(2007)

Similarity indices for high-dimensional data are very desirable for building content-based search systems for featurerich data such as audio, images, videos, and other sensor data. Recently, locality sensitive hashing (LSH) and its variations have been proposed as indexing techniques for approximate similarity search. A significant drawback of these approaches is the requirement for a large number of hash tables in order to achieve good search quality. This paper proposes a new indexing scheme called multi-probe LSH that overcomes this drawback. Multi-probe LSH is built on the well-known LSH technique, but it intelligently probes multiple buckets that are likely to contain query results in a hash table. Our method is inspired by and improves upon recent theoretical work on entropy-based LSH designed to reduce the space requirement of the basic LSH method. We have implemented the multi-probe LSH method and evaluated the implementation with two different high-dimensional datasets. Our evaluation shows that the multi-probe LSH method substantially improves upon previously proposed methods in both space and time efficiency. To achieve the same search quality, multi-probe LSH has a similar timeefficiency as the basic LSH method while reducing the number of hash tables by an order of magnitude. In comparison with the entropy-based LSH method, to achieve the same search quality, multi-probe LSH uses less query time and 5 to 8 times fewer number of hash tables.
SmartStore: a new metadata organization paradigm with semantic-awareness for next-generation file systems
(2009)

Existing storage systems using hierarchical directory tree do not meet scalability and functionality requirements for exponentially growing datasets and increasingly complex queries in Exabyte-level systems with billions of files. This paper proposes semantic-aware organization, called SmartStore, which exploits metadata semantics of files to judiciously aggregate correlated files into semantica-ware groups by using information retrieval tools. Decentralized design improves system scalability and reduces query latency for complex queries (range and top-k queries), which is conducive to constructing semantic-aware caching, and conventional filename-based query. SmartStore limits search scope of complex query to a single or a minimal number of semantically related groups and avoids or alleviates brute-force search in entire system. Extensive experiments using real-world traces show that SmartStore improves system scalability and reduces query latency over basic database approaches by one thousand times. To the best of our knowledge, this is the first study implementing complex queries in large-scale file systems.
Hierarchical ALS Algorithms for Nonnegative Matrix and 3D Tensor Factorization
(2007)

In the paper we present new Alternating Least Squares (ALS) algorithms for Nonnegative Matrix Factorization (NMF) and their extensions to 3D Nonnegative Tensor Factorization (NTF) that are robust in the presence of noise and have many potential applications, including multi-way Blind Source Separation (BSS), multi-sensory or multi-dimensional data analysis, and nonnegative neural sparse coding. We propose to use local cost functions whose simultaneous or sequential (one by one) minimization leads to a very simple ALS algorithm which works under some sparsity constraints both for an under-determined (a system which has less sensors than sources) and over-determined model. The extensive experimental results confirm the validity and high performance of the developed algorithms, especially with usage of the multi-layer hierarchical NMF. Extension of the proposed algorithm to multidimensional Sparse Component Analysis and Smooth Component Analysis is also proposed.
Multi-Scale DenseNet-Based Electricity Theft Detection
(2018)

1. Beijing University of Posts and Telecommunications, Beijing, 100876, China deepblue.lb@gmail.com 2. School of Computer, National University of Defense Technology, Changsha, 410073, China kelele.xu@gmail.com 3. School of Information Communication, National University of Defense Technology, Wuhan, 430015, China 4. The University of Melbourne, Parkville, 3010, Australia yihengw1@student.unimelb.edu.au 5. China Minsheng Bank, Beijing 100031, China wangyanbo@cmbc.com.cn
Automatic PCB Defects Detection and Classification using Matlab
(2014)

The importance of the Printed Circuit Board inspection process has been magnified by requirements of the modern manufacturing environment. In electronics mass production manufacturing facilities, an attempt is often to achieve 100% quality assurance. In this work Machine Vision PCB Inspection System is applied at the first step of manufacturing. In this system a PCB inspection system is proposed and the inspection algorithm mainly focuses on the defect detection and defect classification of the defects. Defect classification is essential to the identification of the defect sources. The purpose of the system is to provide the automatic defect detection of PCB and relieve the human inspectors from the tedious task of finding the defects in PCB which may lead to electric failure. We first compare a standard PCB inspection image with a PCB image to be inspected. The MATLAB tool is used to detect the defects and to classify the defects. With the solutions designed and implemented in this thesis the algorithm designed in the proposed system is able to detect and classify all the known 14 types of defects successfully with greater accuracy. The algorithm makes use of image subtraction method for defect detection and kNN classification algorithm for the classification of the defects. This thesis will present and analyze the performance of the proposed inspection algorithm. The experiment will measure the accuracy of the system.
A compact broadband hybrid ridged SIW and GCPW coupler
(2016)

In this paper, a novel hybrid ridged substrate integrated waveguide (RSIW) and ground coplanar waveguide (GCPW) structure coupler is presented. In this coupler, the RSIW and GCPW are arranged back to back to realize high-integration. Such hybrid coupler combines the broadband advantage of RSIW and the flexibility of GCPW. According to the simulated results, it can operate at 28 GHz and achieve a relative bandwidth of 17%.
Automatic detection of users’ skill levels using high-frequency user interface events
(2010)

Computer users have different levels of system skills. Moreover, each user has different levels of skill across different applications and even in different portions of the same application. Additionally, users’ skill levels change dynamically as users gain more experience in a user interface. In order to adapt user interfaces to the different needs of user groups with different levels of skills, automatic methods of skill detection are required. In this paper, we present our experiments and methods, which are used to build automatic skill classifiers for desktop applications. Machine learning algorithms were used to build statistical predictive models of skill. Attribute values were extracted from high frequency user interface events, such as mouse motions and menu interactions, and were used as inputs to our models. We have built both task-independent and task-dependent classifiers with promising results.
Balanced outcomes in social exchange networks
(2008)

The study of bargaining has a long history, but many basic settings are still rich with unresolved questions. In particular, consider a set of agents who engage in bargaining with one another,but instead of pairs of agents interacting in isolation,agents have the opportunity to choose whom they want to negotiate with, along the edges of a graph representing social-network relations. The area of network exchange theory in sociology has developed a large body of experimental evidence for the way in which people behave in such network-constrained bargaining situations, and it is a challenging problem to develop models that are both mathematically tractable and in general agreement with the results of these experiments.
 We analyze a natural theoretical model arising in network exchange theory, which can be viewed as a direct extension of the well-known Nash bargaining solution to the case of multiple agents interacting on a graph. While this generalized Nash bargaining solution is surprisingly effective at picking up even subtle differences in bargaining power that have been observed experimentally on small examples, it has remained an open question to characterize the values taken by this solution on general graphs, or to find an efficient means to compute it.
 Here we resolve these questions, characterizing the possible values of this bargaining solution, and giving an efficient algorithm to compute the set of possible values. Our result exploits connections to the structure of matchings in graphs, including decomposition theorems for graphs with perfect matchings, and also involves the development of new techniques. In particular, the values we are seeking turn out to correspond to a novel combinatorially defined point in the interior of a fractional relaxation of the matching problem.